Diagnosis Of Heart Disease Using Machine Learning Classification Technique In E-Healthcare (Journal of Pharmaceutical Negative Results, 2023)
Machine learning is a new discipline in computer science that has given learning a new dimension. It emulates human intelligence in the system by learning from previous data. Due to its self-adaptive capabilities and mathematical foundation, machine learning has several applications in healthcare, sentiment analysis, recommendation systems, natural language processing, information retrieval, gameplay, market research, text recognition, and computer vision. Machine learning may be used to enhance healthcare services. Machine learning decision support systems may be used to diagnose various illnesses. This article's study focuses on detecting heart disease using a machine learning technique. CAD (coronary artery disease) is a kind of heart disease that, if not treated promptly, may result in cardiac arrest. It is the leading cause of death globally.CAD is caused by a blockage in the arteries that provide blood to the heart muscles. Plaque deposition, composed of cholesterol and calcium, causes artery blockages. Atherosclerosis is the medical term for this condition. The endothelium is a thin strip of cells that causes smooth blood flow through arteries. Plaque accumulates in the arterial wall when the endothelium deteriorates. This plaque formation affects the blood flow to the heart, causing blood cells to weaken. CAD worsens with time, and if not treated promptly, symptoms might deteriorate. Plaque accumulates over time, hardening plaque. Platelets may cling together to create blood clots in arteries if the plaque ruptures. It has the potential to entirely cut off the blood flow, resulting in a heart attack. Angiography is a common approach for diagnosing CAD. This procedure is invasive and has certain adverse effects. As a result, non-invasive approaches for diagnosing CAD based on clinical data are required. This article provides an ET-SVMRBF (extra tree support vector machine radial basis function) technique for diagnosing CAD using clinical deed. This approach was validated using the Z-Alizadeh Sani CAD dataset from UCI (University of California, Irvine). The primary goal for introducing this technology is to minimize mortality by detecting CAD early.